Relative speed of processing affects interference in Stroop and picture–word interference paradigms: evidence from the distractor frequency effect

Abstract

The Stroop and picture–word interference (PWI) paradigms play a pivotal role in theorising about cognitive processes in general, and language production in particular. A common assumption is that the same mechanisms underlie performance in these two paradigms. Despite this assumption there exist empirical discrepancies. Here we focused on providing a unifying account for the contrasting effects of distractor word frequency in the two paradigms. In four experiments, we addressed whether the contrast is due to inherent design differences between the two paradigms (i.e., grammatical class, stimulus display and relative speed of processing). The results showed that the distractor frequency effect is contingent upon the overall naming latencies in the paradigm, suggesting that the speed of processing target colours and pictures relative to distractor words affects performance in the two tasks. The implications of these results for various models of language production are discussed.

Keywords:

• the picture–word interference paradigm
• Stroop
• distractor frequency effect

Acknowledgements

We thank Steve Monsell, Wido La Heij and three anonymous reviewers for their insightful comments and helpful suggestions. We also thank Enstin Ye and Victoria Walker for collecting the data. Part of these results was presented at the 50th Annual Meeting of the Psychonomic Society, Boston, MA, November 21, 2009. This work was supported by a grant [RYC-2011-08433] from The Spanish Ministry of Economy and Competitiveness to N.J.

Notes

1. Following Ulrich and Miller (1994), we also performed t-test and ANOVA analyses of RTs on the untrimmed data (i.e., the latency data included RTs more than three standard deviations away from the mean), for all comparisons across Experiments 1–4. For all comparisons, trimmed and untrimmed statistical analyses produced the same pattern of significant and non-significant effects at the p < 0.05 level.

2. RTs slow down for easy trials but not to the degree that they match the difficult trials (see Kinoshita & Mozer, 2006, Lupker, Brown, & Colombo, 1997).

3. We combined all experiments for the RT distribution analysis, as the power to detect effects was small for each experiment (around 0.18), and much larger when experiments were combined (0.72). Following Ulrich and Miller (1994), we presented a distribution analysis on the untrimmed data (i.e., the latency data including those that are more than three standard deviations away from the mean). Whether the distribution analysis was with trimmed or untrimmed data, we found similar results which are consistent with our predictions (i.e., no distractor frequency effect when naming is fast and a significant distractor frequency effect when naming is relatively slow).